63 postdoc-computational-fluid-dynamics-"Prof" positions at King Abdullah University of Science and Technology
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containment. The prohibitively high computational cost of such simulations necessitates the development of efficient and robust surrogate models for general GCS modeling tasks, especially when inverse modeling
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research in the field of machine learning, more specifically, deep learning and representation learning architectures. Application areas of ML include, but are not limited to, computer vision, natural
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Prof. Husam N. Alshareef , Chair of the Center of Excellence for Renewable Energy and Storage Technologies (CREST) at KAUST, is seeking postdoctoral researchers with experience in the field
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Prof. Husam N. Alshareef , Chair of the Center of Excellence for Renewable Energy and Storage Technologies (CREST) at KAUST, is seeking highly qualified postdoctoral researchers for Hard Carbon
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The Applied Mathematics and Computational Sciences (AMCS) program in the Computer, Electrical and Mathematical Sciences and Engineering Division (https://cemse.kaust.edu.sa ) at King Abdullah
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This postdoc position is designed for a specialist in the fabrication and development of large-scale perovskite solar cells and modules. For this position, we are looking for a candidate with an
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to the transition towards net-zero carbon emissions? The group of Prof. Anqi Wang at KAUST is recruiting several postdocs to join our dynamic team. Our research is highly collaborative and interdisciplinary
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This postdoc position is for a specialist in the fabrication of silicon heterojunction solar cells to be integrated into monolithic silicon perovskite tandem solar cells. For this position, we
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Professor Volker Vahrenkamp’s research team is currently recruiting for a 2-year Post-doc position in the dynamic carbonate research group (https://caress.kaust.edu.sa/ ) to study the shallow water
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to multi-omics single cell data. The 3 positions require skills (each in different degrees and balance) on high-performance computing, dynamical systems, machine learning techniques for high-dimensionality